Design of a high-tech system to evaluate fresh strawberry quality-based on the principal component analysis

Abstract

This study was designed to investigate the differences in the physicochemical properties among twelve strawberry cultivars by using pattern recognition tools, to provide a theoretical basis for quality variation among samples. The data of 14 indicators were subjected to principal component analysis (PCA), descriptive statistical analysis, correlation analysis, and hierarchical cluster analysis, to filter core evaluation factors. Quality evaluation index weights and quality comprehensive evaluation were determined by an analytic hierarchy process. Of the 14 indicators selected as indices of fresh strawberry quality evaluation index, including a value, sugar-acid ratio, firmness, vitamin C, TAC, and TPC. The data were deployed to adjust the multivariate kinetics using Analytic Hierarchy Process (AHP), and the results were compared to those sensory score using sensory personnel. Results showed that the correlation coefficient of sensory scores and AHP comprehensive score is 0.9239. This high correlation coefficient indicates that the use of our mathematical model for strawberry quality evaluation is feasible. The information herein provides a practical strategy for the evaluation of strawberry quality.

Keywords

Fresh strawberry strawberry quality analytic hierarchy process sensory analysis practical strategy

1. Introduction

Strawberry (Fragaria $\times$ ananassa Duch.) is one of the most commonly consumed berries in the word, largely due to its appealing taste and attractive surface colour [1]. Among the various fruit characteristics, consumers prefer that fruits be of high quality, have a favourable taste and texture, be free of contaminants, and contain nutritional and health promoting substances; Indeed, in addition to the external appearance, the internal quality of fruit is becoming increasingly important. It is rich in micronutrients and phytochemical compounds, particularly anthocyanins, vitamin C, sugar, acid, and phenolic compounds. The levels of these phytochemicals strongly influence sensorial-organoleptic attributes and nutritional value [2, 3, 4].

Currently, most studies on strawberry focus on fruit processing and storage [5, 6, 7]. Very few studies have performed comprehensive quality evaluations of raw material. Fruit quality is the most important parameter to consider when evaluating fruit and vegetable cultivars [8, 9]. Evaluation of the quality of strawberries is important for the selective breeding of better strawberry cultivars, as well as for the selection of preferred fruits. During quality evaluation, a large number of variables or factors are analysed by statistical tests to reduce dimensionalities, and ultimately, to present the results in graphical format [10]. Moreno et al. [11] performed quality evaluation using principal component analysis (PCA) to select high-quality materials for tomato processing. Nogales-Delgado et al. [12] performed quality evaluation for different nectarine cultivars using PCA to select the optimal fresh-cut processing material.

To comprehensively describe the qualitative characteristics of fruit, physicochemical and sensory analyses are needed. The chemical composition of strawberries varies significantly with genotype [13, 14, 15, 16, 17]. Statistical processing of such a large amount of heterogeneous chemical data according to classical methods provides a rich source of information on all tested variables [18]. However, this approach does not provide a global knowledge of the relationships between these different chemicals, nor does it allow the grouping of samples with homogeneous characteristics. In such situations, multivariate statistical methods are often employed, which allow for variable reduction and facilitate the presentation of results in a clear, graphical manner [19]. One such example is PCA, which permits the identification of the most important directions of variability in a multivariate data matrix and allows graphical representation of the results.

PCA is a mathematical tool that reduces the dimensionality of data and allows the visualization of underlying structures in experimental data and relationships between data and samples. It has been applied to the characterization of textural properties of select ready-to-eat meat products [20]. For instance, Keenan et al. [21] used multivariate analyses of physicochemical properties to select apple cultivars for use in ready-to-eat desserts. Furthermore, PCA has been widely used in property evaluation of wheat, corn, peanuts, and other crops, as well as in the comprehensive evaluation of germplasm resource [22, 23].

The present study was designed to use chemometric tools to analyse differences in fruit quality based on the physical and chemical properties of several strawberry varieties. Fruit weight, firmness, vitamin C, total anthocyanin content, total phenolic content (TPC), pH, crude protein, water, L value, a*, b*, total sugar/total acid, and total soluble solids/total acids were all considered. They are used to evaluate the quality of strawberry cultivars using principal component analysis (PCA), descriptive statistical analysis, correlation analysis, and hierarchical cluster analysisd and compared the results with the results of sensory analysis. The results of this approach provide a solid theoretical basis than can be applied during the evaluation of strawberry quality and eventual selection for culture, harvesting, and consumption, in order to find the best hybrid strawberry for production.

2. Materials and methods

2.1 Strawberry samples

Strawberry fruits are susceptible to the effect of environment, soil, water and other factors [24]. Red-freshed strawberries (Fragaria $\times$ ananassa Duch.) of the twelve cultivars harvested from Strawberry planting base (Shenyang, China). All strawberry plants were cultivated in the open field, which coincided with the commercial harvesting period for the twelve varieties. All fruits were harvested at full mature stage (completely red); Strawberries were picked for each cultivar at physiological maturity and stored at 4 ${}^{\circ}$ C. The fruit samples were removed from cold storage and held at room temperature before measurement, and some strawberries were selected randomly to assess each cultivar’s characteristics.

Details of physical and chemical properties is as shown in supporting file.

2.2 Sensory analysis

Sensory evaluation is a comprehensive and objective evaluation of the fruit colour, size, appearance, texture, flavour, and odour of fruit through multiple organs such as vision, smell, touch, taste, and hearing. Real data will be obtained for comprehensive evaluation through mathematical statistics. The quantitative descriptive analysis (QDA ${}^{\@setsize{\scriptsize}{9.5pt}{\viiipt}{\@viiipt}\textregistered}$ ) method was used to train the sensory panel and perform the sensory analysis according to set sensory standards [25]. Descriptive analysis was performed by a panel of ten trained assessors (five female and five male, 20–40 years). Stimulation of the senses of the assessors by the test samples was standardized. After assessors reached a consensus, each cultivar was scored. The assessors were not allowed to communicate with each other during sensory evaluation; this helped eliminate any bias or influence in scoring. All samples were randomly numbered and placed, and they were evaluated, in turn, by the team memb; each sample was evaluated twice. For this study, five scoring properties were set for evaluation: fruit colour, size, appearance, texture, flavour, and odour. (As shown in Table S1 of supporting file).

Sensory evaluation was conducted on five properties of strawberry fruit, including colour, size, texture, flavour, and odour. Each property was divided into five grades based on the quality. To better reflect the differences in senses between cultivars, the fruits were graded into five levels (5, 4, 3, 2, and 1) from the best to the worst quality, respectively. Before evaluation, a scoring system was developed, based on which the assessors performed the sensory evaluation. The development of the form followed these principles:

(1)
All selected sensory properties reflect the unique characteristics of strawberry raw material or a certain feature.
(2)
The sensory properties should have correlations with the measurements determined by instruments.
(3)
The sensory score form should include a description of the quality grading and the corresponding scores.

2.3 Analytic hierarchy process (AHP)

The basic principle of analytic hierarchy process (AHP) is based on the evaluation system related to decision-making. Through expert consultation, the relative importance of elements at each level is analyzed and compared to each other. These data are used to form a judgment matrix, and then the component of the corresponding characteristic direction of the maximum characteristic root of the judgment matrix, which is used as the corresponding coefficient. Finally, the weight of the scheme layer relative to the target layer is judged, which is a decision-making method combining qualitative analysis and quantitative analysis. In the process of quality evaluation, before using analytic hierarchy process, each evaluation index should be centrally normalized. And the relative importance of each evaluation index is finally determined by comprehensively consulting the opinions put forward by experts and consulting the relevant literature. In this way, the comparison matrix of evaluation indexes should be established, and the weight value of each evaluation index should be determined by using the comparison matrix. Finally, each normalization index is weighted and summed to obtain the comprehensive scores and ranking of different evaluation products.

2.4 Data analysis

All measurements were performed three times. Pattern recognition methods were applied to data collection as previously reported [26, 27]; These included PCA as an unsupervised classification method and HCA as an unsupervised learning method. PCA and HCA were performed using the SPSS21.0 software package. PCA transforms the original, measured variables into new uncorrelated variables called principal components [27, 28, 29]. The first principal component covers as much of the variation in the data as possible, whereas the second principal component is orthogonal to the first and covers as much of the remaining variation as possible, and so on. HCA calculates the distances (or correlation) between all samples using a defined metric, such as Euclidean distance and Manhattan distance [28]. HCA is the most common approach, wherein clusters are formed sequentially. The most similar objects are first grouped, and these initial groups are merged according to their similarities. Eventually, as the similarity decreases, all subgroups are fused into a single cluster. PCA has previously been used to characterize quince fruit [30, 31]. PCA and HCA have also been used to classify fruits, vegetables, and spices based on their in vitro antioxidant activities [27, 32]. The statistical software SPSS21.0 was used for data processing and statistical analysis.

2.5 Data standardization

(1) Initialization of quality evaluation indices

To eliminate the impact of different dimensions and magnitudes on quality evaluation, the quality evaluation indices were initialized. Initialization involves the use of the absolute value of the difference between the actual value and the ideal value of the quality evaluation index. After initialization of the quality evaluation indices, the closer the original value is to the ideal value, the smaller the initialized value is. The initialized value $x^{\prime}_{i}\geqslant 0$ :

$\displaystyle x^{\prime}_{i}=\left|1-\frac{x_{i}}{x_{0}}\right|$ (1)

$x^{\prime}_{i}$ : initialized value of quality evaluation index $x_{i}$ : actual value of quality evaluation index $x_{0}$ : ideal value of quality evaluation index

(2) Positizing and standardization

After initialization, the value range of the quality evaluation indices was $x^{\prime}_{i}\geqslant 0$ . The closer the original value was to the ideal value, the smaller the initialized value. To facilitate the comprehensive evaluation, the values were positized. In addition, to avoid the impact of different magnitudes on the comprehensive evaluation, the values were standardized. The method of forward control and standardization was as follows:

$\displaystyle X_{i}=\frac{x^{\prime}_{i}}{x^{\prime}_{i\max}}$ (2)

The effect of positizing and standardization of the quality evaluation indices was to bring original values that were close to the ideal level (in either the positive or negative direction) closer to 1. Thereby, the standardized value range was $0\leqslant X_{i}\leqslant 1$ .

3. Results and discussion

3.1 Physical and chemical characteristics of 12 strawberry varieties

The composition of twelve strawberry cultivars is presented in Table 1. The coefficient of variance (CV) was used to weigh each variable. The CVs of 14 properties ranged from 1.31% to 23.72%. TSS, TA, water content, and L value showed little variation among the different strawberry varieties ( $\leqslant$ 10%), with respective CVs of 6.93%, 3.90%, 1.10%, and 9.03%. Strikingly, however, variation in other parameters, including fruit weight, V ${}_{\text{C}}$ , TAC, TPC, TS, TA, TS/TA, and a ${}^{\ast}$ and b ${}^{\ast}$ was higher than 10%. Fruit properties are known to be affected by cultivar, growing region, climate, cultivar practices, and harvest maturity [32]. In this study, all samples had the same cultivation, management, and picking conditions; thus, all differences were entirely due to genetic diversity.

In order to determine the variables that are most suitable for the evaluation of fruit quality, we performed correlation analysis using pooled data. Table 2 shows the correlation coefficients between fourteen variables for the twelve samples. Most mechanical variables were significantly correlated ( $R=$ 0.78, $P<$ 0.01) with fruit weight and soluble sugar. Additionally, a higher fruit weight was positively correlated with the amount of crude protein. Crude protein and firmness were negatively correlated ( $R=-$ 0.73, $P<$ 0.01). Firmness and $a$ value were positively correlated ( $R=$ 0.72, $P<$ 0.01); This variable was also significantly negatively correlated with TS/TA. These results show that firmness influences the colour and taste of strawberries.

Table 1
Descriptive statistics of strawberry physicochemical characteristics

Indicator	Mean	Coefficient of variation/%	Standard deviation
Fruit weight (g)	12.91	21.57	2.78
Firmness (N)	5.30	19.27	1.02
V ${}_{\text{C}}$ (mg.100 g ${}^{-1}$ )	63.38	14.71	9.32
TAC (mg.100 g ${}^{-1}$ )	5.29	19.67	1.04
TPC (mg.100 g ${}^{-1}$ )	167.86	11.35	19.05
TSS	6.99	6.93	0.48
TS	5.52	13.90	0.77
TA	0.55	19.67	0.10
CP (%)	0.68	3.90	0.03
TS/TA	10.39	23.72	2.46
Water content (%)	90.43	1.10	0.96
L value/L	47.74	9.03	4.31
$a$ value/a ${}^{\ast}$	24.13	17.16	4.14
$b$ value/b ${}^{\ast}$	16.49	13.85	2.28

Table 2

Correlation coefficients between the strawberry quality variables ${}^{\text{a}}$

	X1		X2		X3		X4		X5		X6		X7		X8		X9		X10		X11		X12		X13		X14
X1	1	.00
X2	$-$ 0	.53	1	.00
X3	$-$ 0	.27	$-$ 0	.35	1	.00
X4	0	.18	$-$ 0	.07	0	.07	1	.00
X5	$-$ 0	.54	0	.11	0	.09	0	.08	1	.00
X6	0	.49	0	.01	0	.04	0	.41	$-$ 0	.39	1	.00
X7	0	.78 ${}^{\ast\ast}$	$-$ 0	.58 ${}^{\ast}$	0	.15	0	.26	$-$ 0	.37	0	.33	1	.00
X8	$-$ 0	.16	0	.28	0	.20	0	.19	0	.13	0	.20	0	.15	1	.00
X9	0	.62 ${}^{\ast}$	$-$ 0	.73 ${}^{\ast\ast}$	0	.25	0	.17	$-$ 0	.32	0	.35	0	.69 ${}^{\ast}$	0	.12	1	.00
X10	0	.55	$-$ 0	.58 ${}^{\ast}$	$-$ 0	.01	0	.02	$-$ 0	.25	$-$ 0	.02	0	.49	$-$ 0	.76 ${}^{\ast\ast}$	0	.20	1	.00
X11	0	.11	$-$ 0	.26	0	.20	0	.43	0	.29	0	.23	0	.36	0	.59 ${}^{\ast}$	0	.18	$-$ 0	.19	1	.00
X12	0	.28	$-$ 0	.32	$-$ 0	.36	0	.15	0	.26	0	.07	0	.21	$-$ 0	.12	0	.50	0	.13	0	.03	1	.00
X13	$-$ 0	.08	0	.72 ${}^{\ast\ast}$	$-$ 0	.54	0	.01	$-$ 0	.30	0	.42	$-$ 0	.39	$-$ 0	.02	$-$ 0	.53	$-$ 0	.26	$-$ 0	.20	$-$ 0	.22	1	.00
X14	0	.18	0	.15	0	.04	0	.31	$-$ 0	.39	0	.65 ${}^{\ast}$	0	.13	0	.02	$-$ 0	.04	0	.04	0	.09	$-$ 0	.27	0	.59 ${}^{\ast}$	1	.00

Note: X1: Fruit weight; X2: Firmness; X3: Vitamin C (V ${}_{\text{C}}$ ); X4: Total anthocyanin content (TAC); X5: Total phenolic content (TPC); X6: Total soluble solid (TSS); X7: Soluble sugar (SS); X8: Titratable acid (TA); X9: Crude protein (CP); X10: Sugar-acid ratio; X11: Water content; X12: L value; X13: $a$ value; X14: $b$ value. ${}^{\ast}$ Significant at $P<$ 0.05. ${}^{\ast\ast}$ Significant at $P<$ 0.01.

3.2 Principal component analysis (PCA)

PCA was applied to the standardized values of analysed parameters of the twelve strawberry cultivars. In order to define the most appropriate descriptors of quality of materials, we used the cross-validation technique to establish that five principal components are sufficient to account for the total variability. The results of the calculations are presented in Tables 3 and 4.

PC1 explained 27.552% of the total variance in the data set, PC2 explained 19.710%, PC3 explained 15.191%, PC4 explained 12.386%, and PC5 explained 11.655%. The accumulative variance contribution of these five main components extracted by PCA was 86.494%. In other words, 86.494% of the total variance in the 14 considered variables could be condensed into five new variables (PCs).

Scree plots can be used to determine the optimal number of principal components. In the scree plot, the steepest part of the eigenvalue slope corresponds to the number of principal components. Figure 1 shows the scree plot of evaluation indexes upon PCA of samples; this reveals that the first five principal components yielded a greater coefficient of variation, while attachment is relatively steep. The first five principal components ( $\lambda>$ 1) are shown in Table 3; It was the most appropriate extraction for five principal components. The accumulative variance contribution by PCA was 86.494%, and thus the analysis accounted for most of the information that explains strawberry quality. Therefore, we decided to reduce the previous 14 variables to five new variables. The result of PCI indicated that it was feasible and reasonable to divide the ten properties into five components through cluster analysis.

Table 3
Results from the principal component analysis of the first three principal components

Component	Eigenvalues	Variance (%)	Cumulative (%)
1	3.857	27.552	27.552
2	2.759	19.710	47.262
3	2.127	15.191	62.453
4	1.734	12.386	74.839
5	1.632	11.655	86.494

Table 4

Component score coefficient matrix of PCA

Properties	Component
	PC1	PC2	PC3	PC4	PC5
Fruit weight	0.79	0.36	$-$ 0.24	$-$ 0.01	0.29
Firmness	$-$ 0.78	0.28	0.36	$-$ 0.17	0.19
V ${}_{\text{C}}$	0.18	$-$ 0.17	0.08	0.17	$-$ 0.88
TAC	0.11	0.28	$-$ 0.01	0.81	0.04
TPC	$-$ 0.41	$-$ 0.65	0.09	0.52	0.08
TSS	0.32	0.76	0.16	0.28	0.09
SS	0.86	0.17	$-$ 0.05	0.17	$-$ 0.06
TA	0.07	0.05	0.94	0.22	$-$ 0.14
CP	0.93	$-$ 0.06	0.11	0.05	0.04
Sugar-acid ratio	0.40	0.03	$-$ 0.88	0.01	$-$ 0.03
Water content	0.25	$-$ 0.02	0.40	0.70	$-$ 0.15
L	0.38	$-$ 0.33	$-$ 0.05	0.24	0.73
a ${}^{\ast}$	$-$ 0.53	0.74	0.08	$-$ 0.13	0.34
b ${}^{\ast}$	$-$ 0.06	0.86	$-$ 0.08	0.22	$-$ 0.16

Figure 1.

Scree plot of evaluation indexes of strawberry by principal component analysis.

The component score coefficient matrix of the five main principle components is shown in Table 4. The data reveal that the most important variables for the PC1 were fruit weight, firmness, soluble sugar (SS) and crude protein (CP), among which fruit weight, soluble sugar (SS), and crude protein (CP) were positively correlated with PC1. PC2 mainly accounted for total phenolic content (TPC), total soluble solid (TSS), and colour (a ${}^{\ast}$ and b ${}^{\ast}$ ). There was a significant positive correlation between PC2 and total soluble solid (TSS) or colour (a ${}^{\ast}$ and b ${}^{\ast}$ ). PC3 mainly accounted for titratable acid (TA) and sugar-acid ratio. PC3 was positively correlated with titratable acid (TA). PC4 mainly accounted for total anthocyanin content (TAC) and water content, which were positively correlated with PC4. PC5 mainly accounted for Vitamin C (V ${}_{\text{C}}$ ) and L value. PC5 was positively correlated with L value, but negatively correlated with V ${}_{\text{C}}$ .

After standardization of the original data, using the Euclidean distance model and the between-group average linkage cluster analysis, all quality indices were clustered into five principle components on the basis of PCA results. After cluster analysis, a dendrogram was automatically calculated and generated by SPSS software in Fig. 2.

Figure 2.

Clustering dendrogram of the fourteen evaluation factors.

A hierarchical agglomerative procedure was employed to establish clusters. Samples were grouped on the basis of similarities in hierarchical cluster analysis, without taking into account the information regarding class membership. The results obtained following HCA are shown as a dendrogram in Fig. 2 in which five well-defined clusters are visible. Samples are grouped in clusters in terms of their nearness or similarity. Cluster analysis (CA) uses less information (distances only) than PCA. The first group of samples is clearly discernible and is composed of water, SS, CP, and sugar-acid ratio (SA). These species are associated with high firmness and lower PH, TS/TA, and fruit weight. This is in agreement with the results of the PCA. The second cluster consists of firmness and a ${}^{\ast}$ . The third cluster consists of TPC and L value. The fourth cluster consists of SS, b ${}^{\ast}$ and TAC. The fifth cluster consists of TA, water and V ${}_{\text{C}}$ . In the first cluster, the sugar-acid ratio showed the highest coefficient of variation, indicating a significant difference between varieties. The sugar-acid ratio was therefore selected to represent the other three indices. In the second cluster, there was a significant positive correlation between the firmness and $a$ value; Therefore, the information of one index can be replaced by the other. Since the coefficient of variation of the firmness was higher than that of $a$ value, firmness was selected to represent the second cluster. In the third cluster, the coefficient of variation of TPC was higher than that of L value, indicating a significant difference in TPC between varieties. Therefore, TPC was selected to represent the third cluster. In the fourth cluster, the coefficient of variation of TAC was higher than the other two indices, and was therefore selected to represent the fourth cluster. In the fifth cluster, coefficients of variation of both TA and V ${}_{\text{C}}$ were greater than 10%. The sugar-acid ratio in the first cluster determines the taste of the fruit, which already contains TA information. V ${}_{\text{C}}$ was therefore selected to represent the fifth cluster. Strawberry colour is an important indicator of sensory quality, and it is a major factor affecting consumer choice. Six core indices of strawberry quality evaluation including sugar-acid ratio, firmness, TPC, TAC, V ${}_{\text{C}}$ , and $a$ value were selected.

3.3 Establishment of quality evaluation of different fresh strawberry varieties

Due to the different characteristics of each index, the ideal value of each index (x0) was first determined. Among all indices, colour, TPC, TAC and V ${}_{\text{C}}$ were positive indices; The bigger the determined value, the better the quality. The firmness and the sugar-to-acid ratio of fruits were neutral indices, and for the firmness of fruits, a measurement close to 5.29 was ideal, while for the sugar-acid ratio, a measurement close to 10.39 was ideal (As shown in Table S2 of supporting file). During comprehensive evaluation screening, in order to eliminate the effects of different measurement units and different data dimensions between indices, raw data required standardization before calculation [33]. The raw data of six core quality indices from 12 strawberry varieties were standardized with a formula, and the results are shown in Table 5.

Table 5
Result of data standardization of strawberry

	$a$ value	Sugar-acid ratio	Firmness	V ${}_{\text{C}}$	TAC	TPC
Akihime	0.00	0.85	0.79	0.56	0.64	0.91
All-star	0.79	0.85	0.66	0.23	0.99	0.62
99	0.68	0.76	0.19	0.38	0.39	0.58
R7	1.00	0.77	0.36	0.57	0.14	0.41
Ever bearers	0.88	0.47	0.00	0.74	0.55	1.00
Tudla	0.72	0.95	0.50	1.00	1.00	0.03
Toyonoka	0.67	1.00	0.64	0.59	0.43	0.22
Darselect	0.62	0.21	0.38	0.72	0.67	0.30
Hani	0.75	0.87	0.57	0.51	0.48	0.08
Hokowase	0.39	0.00	0.52	0.69	0.00	0.25
Flange	0.73	0.89	0.35	0.00	0.26	0.00
Sweet charlie	0.95	0.71	0.87	0.28	0.95	0.21

The contribution and the importance of the six core quality evaluation indices were combined with the experts’ experience to generate a hierarchical relational structure and to construct a judgement matrix using a 1–9 scale method in Table 6. The weight (WI) of each index was calculated with the pairwise comparison matrix. The proportional scaling number indicated the relative importance of the index.

Table 6

Pairwise comparison matrix and its consistency

	$a$ value	sugar-acid ratio	Firmness	V ${}_{\text{C}}$	TAC	TPC
$a$ value	1	1	3	5	7	9
Sugar-acid ratio	1	1	3	5	7	9
Firmness	1/3	1/3	1	3	4	5
V ${}_{\text{C}}$	1/5	1/5	1/3	1	1	2
TPC	1/7	1/7	1/4	1	1	1
TAC	1/9	1/9	1/5	1/2	1	1
CR $=$ 0.057 (CR $<$ 0.1)

CR $<$ 0.1 indicated that the weight of each index was reasonable. The weights of each index are listed in Table S3 of supporting file.

3.4 Establishment of the discriminant function for strawberry quality

The sensory score sheet of strawberry raw material was added in Table S4 of supporting file. The composite score Y of each strawberry variety was calculated by multiplying the weight of each strawberry raw material’s quality evaluation index by the standardized score of each quality index in Table 7.

Y $=$ 0.311358 $\times$ a value $+$ 0.311358 $\times$ Sugar-acid ratio $+$ 0.167618 $\times$ Firmness $+$ 0.084058 $\times$ VC $+$ 0.068368 $\times$ TAC $+$ 0.057249 $\times$ TPC.

Table 7
Composite score and sensory score of twelve strawberry varieties

Strawberry	Colour	Size	Texture	Flavour	Odour	Sensory score	Composite score
Akihime	3.51	3.25	3.02	3.00	2.47	15.25	0.54
All-star	3.75	4.30	3.55	3.06	3.37	18.03	0.74
99	4.00	3.30	3.50	3.00	2.31	16.11	0.57
R7	3.06	3.50	3.07	3.52	3.94	17.09	0.69
Ever bearers	3.57	3.03	3.20	3.62	3.08	16.50	0.58
Tudla	3.52	3.26	4.00	3.55	3.97	18.30	0.76
Toyonoka	3.85	3.59	3.59	3.07	3.15	17.25	0.72
Daselect	3.05	3.50	3.06	3.00	2.40	15.01	0.45
Hani	3.55	3.05	3.42	3.50	3.25	16.77	0.68
Hokowase	3.55	2.43	3.69	2.50	2.59	13.58	0.28
Flange	3.25	3.06	3.75	2.25	3.54	15.85	0.58
Sweet charlie	4.25	3.49	3.72	3.90	3.52	18.88	0.76

3.5 Validation of the model for comprehensive evaluation of strawberry raw material

In order to investigate the accuracy of AHP integrated value rating model and its results for strawberry raw material quality, sensory evaluation of strawberry raw material was conducted, and the model was validated by standardized sensory scoring. Since the difference between individual evaluators and their personal preferences may lead to bias in the results, the aim of standardization was to eliminate the impact of individual evaluators. The relationship between the results of sensory evaluation and the calculated results using AHP was analysed by regression analysis in Tables 2–6. The sensory and AHP scores are indicated on the X-axes and Y-axes, respectively. There was a linear relationship between the two variables: y $=$ 10.014x $+$ 10.417, and the correlation coefficient was 0.9239 in Fig. 3. The high correlation indicates that the mathematical model can be used with confidence for quality evaluation of strawberry raw material.

Figure 3.

Fitness test for strawberry of sensory and AHP values.

4. Conclusions

Considerable variations were observed between different strawberry cultivars in terms of physical and chemical properties. The unsupervised pattern recognition techniques of PCA enabled visualization of this complex dataset, and unmasked the underlying relationships that were responsible for the clustering observed. Fourteen properties were reduced to five main components. Twelve strawberry cultivars clustered significantly on PC1, PC2, PC3, PC4, and PC5. The combination of characterization and multivariate data analysis facilitated the inference of similarities and differences between strawberry cultivars based on their physical and chemical properties. This data analysis technique provides powerful insights into the variations of properties between different strawberry cultivars, and thus could be applied to the selection of optimum hybrid strawberry cultivars tailored for different purposes in strawberry breeding programs. Further studies could focus on the evaluation of strawberry quality of different hybrid strawberry cultivars, in order to find the best hybrid strawberry for production. And this approach could be integrated into commercial agricultural programs in order to maximize freshness, harvest timing, and appeal to consumers.

Footnotes

Acknowledgments

Shenyang Bureau of Science and Technology Particularly Dispatched Rural Mission (project number: 20-207-3-46). This project was the key planned project of year 2020 of Department of Science & Technology of Liaoning province (Serial number: 2020JH2/10200039).

References

Tulipani

Mezzetti

Capocasa

Bompadre

Beekwilder

De Vos

Capanoglu

Bovy

Battino

. Antioxidants, phenolic compounds, and nutritional quality of different strawberry genotypes. Journal of Agricultural and Food Chemistry. 2008; 56(3): 696-704. doi: 10.1021/jf0719959.

Tiwari

Patras

Brunton

Cullen

O’Donnell

. Effect of ultrasound processing on anthocyanins and color of red grape juice. Ultrasonics Sonochemistry. 2010; 17(3): 598-604. doi: 10.1016/j.ultsonch.2009.10.009.

Aaby

Wrolstad

Ekeberg

Skrede

. Polyphenol composition and antioxidant activity in strawberry purees; Impact of achene level and storage. Journal of Agricultural and Food Chemistry. 2007; 55(13): 5156-5166. doi: 10.1021/jf070467u.

Medina

. Determination of the total phenolics in juices and superfruits by a novel chemical method. Journal of Functional Foods. 2011; 3: 79-87. doi: 10.1016/j.jff.2011.02.007.

Stübler

Böhmker

Juadjur

Heinz

Rauh

Shpigelman

Aganovic

. Matrix- and technology-dependent stability and bioaccessibility of strawberry anthocyanins during storage. Antioxidants (Basel, Switzerland). 2020; 10(1): 30. doi: 10.3390/antiox10010030.

Yigit

Velioglu

. Effects of processing and storage on pesticide residues in foods. Critical Reviews in Food Science and Nutrition. 2020; 60(21): 3622-3641. doi: 10.1080/10408398.2019.1702501.

Bouzari

Holstege

Barrett

. Vitamin retention in eight fruits and vegetables: A comparison of refrigerated and frozen storage. Journal of Agricultural and Food Chemistry. 2015; 63(3): 957-962. doi: 10.1021/jf5058793.

Vandendriessche

Vermeir

Mayayo Martinez

Hendrickxb

Lammertyna

Nicolaïa

Hertog

MLATM

. Effect of ripening and inter-cultivar differences on strawberry quality. LWT-Food Science and Technology. 2013; 52(2): 62-70. doi: 10.1016/j.lwt.2011.12.037.

Crecente-Campo

Nunes-Damaceno

Romero-Rodrıguez

. Color, anthocyanin pigment, ascorbic acid and total phenolic compound determination in organic versus conventional strawberries. Journal of Food Composition and Analysis. 2012; (28): 2-30. doi: 10.1016/j.jfca.2012.07.004.

10.

Naes

Baardseth

Helgesen

. Multivariate techniques in the analysis of meat quality. Meat Science. 1996; 43(1): 135-149. doi: 10.1016/0309-1740(96)00061-7.

11.

Moreno

Tarquis

. Mulch materials in processing tomato: A multivariate approach. Scientia Agricola. 2013; 70: 250-256. doi: 10.1155/2014/617408.

12.

Nogales-Delgado

Fuentes-Pérez

Ayuso-Yuste

. Study of different nectarine cultivars and their suitability for fresh-cut processing. International Journal of Food Science and Technology. 2014; 49(1): 114-120. doi: 10.1111/ijfs.12282.

13.

Anttonen

Hoppula

Nestby

Verheul

Karjalainen

. Influence of fertilization, mulch color, early forcing, fruit order, planting date, shading, growing environment, and genotype on the contents of selected phenolics in strawberry (Fragaria × ananassa Duch.) fruits. Journal of Agricultural and Food Chemistry. 2006; 54: 2614-2620. doi: 10.1021/jf052947w.

14.

Capocasa

Scalzo

Mezzetti

Battino

. Combining quality and antioxidant attributes in the strawberry: The role of genotype. Food Chemistry. 2008; 111(4): 872-878. doi: 10.1016/j.foodchem.2008.04.068.

15.

Olsson

Ekvall

Gustavsson

Nilsson

Pillai

Sjöholm

Svenson

Akesson

Nyman

. Antioxidants, low molecular weight carbohydrates, and total antioxidant capacity in strawberries (Fragaria × ananassa): Effects of cultivar, ripening, and storage. Journal of Agricultural and Food Chemistry. 2004; 52(9): 2490-2498. doi: 10.1021/jf030461e.

16.

Wang

Zheng

. Effect of plant growth temperature on antioxidant capacity in strawberry. Journal of Agricultural and Food Chemistry. 2001; 49: 4977-982. doi: 10.1021/jf0106244.

17.

Wang

Zheng

Galletta

. Cultural system affects fruit quality and antioxidant capacity in strawberries. Journal of Agricultural and Food Chemistry. 2002; 50: 6534-6542. doi: 10.1021/jf020614i.

18.

Destefanis

Barge

Brugiapaglia

Tassone

. The use of principal component analysis (PCA) to characterize beef. Meat Science. 2000; 56: 255-259. doi: 10.1016/S0309-1740(00)00050-4.

19.

Naes

Baardseth

Helgesen

Isaksson

. Multivariate techniques in the analysis of meat quality. Meat Science. 1996; 43: 135-149.

20.

Probola

Zander

. Application of PCA method for characterisation of textural properties of selected ready-to-eat meat products. Journal of Food Engineering. 2007; 83: 93-98. doi: 10.1016/0309-1740(96)00061-7.

21.

Keenan

Valverde

Gormley

Butler

Brunton

. Selecting apple cultivars for use in ready-to-eat desserts based on multivariate analyses of physico-chemical properties. LWT-Food Science and Technology. 2012; 48: 308-315. doi: 10.1016/j.lwt.2012.04.005.

22.

Upadhyaya

Nigam

Singh

. Evaluation of groundnut core collections to identify sources of tolerance to low temperature at germination. Indian Journal of Plant Genetic Resources. 2001; 14(2): 165-167. doi: 10.3923/ijpbg.2017.31.38.

23.

Soeiro

Boen

Wagner

Lima-Pallone

. Physico-chemical quality and homogeneity of folic acid and iron in enriched flour using principal component analysis. International Journal of Food Sciences and Nutrition. 2009; 60(Suppl 7): 167-179. doi: 10.1080/09637480902769567.

24.

Asami

Hong

Barrett

Mitchell

. Comparison of the total phenolic and ascorbic acid content of freeze-dried and air-dried marionberry, strawberry, and corn grown using conventional, organic, and sustainable agricultural practices. Journal of Agricultural and Food Chemistry. 2002; 51: 1237-1241. doi: 10.1021/jf020635c.

25.

ISO Standard 8586-1, Sensory analysis: general guide to selection, training and monitoring of panellists. I. Selected panellists in ‘French Standard’, 1993.

26.

Patras

Brunton

Downey

Rawson

. Application of principal component and hierarchical cluster analysis to classify fruits and vegetables commonly consumed in Ireland based on in vitro antioxidant activity. Journal of Food Composition and Analysis. 2011; 24(2): 250-256. doi: 10.1016/j.jfca.2010.09.012.

27.

Berrueta

Alonso-Salces

Héberger

. Supervised pattern recognition in food analysis. Journal of Chromatography A. 2007; 1158(1-2): 196-214. doi: 10.1016/j.chroma.2007.05.024.

28.

Çam

Yaşar

Durmaz

. Classification of eight pomegranate juices based on antioxidant capacity measured by four methods. Food Chemistry. 2009; 112: 721-726. doi: 10.1016/j.foodchem.2008.06.009.

29.

Downey

Fouratier

Kelly

. Detection of honey adulteration by addition of fructose and glucose using near infrared transflectance spectroscopy. Journal of Near Infrared Spectroscopy. 2003; 11(1): 447-456. doi: 10.1255/jnirs.395.

30.

Silva

Andrade

Martins

Valentão

Ferreres

Seabra

. Ferreira, M.A. Journal of Agricultural and Food Chemistry. 2005; 53: 111-122. doi: 10.1021/jf040057v.

31.

Hossain

Patras

Barry-Ryan

Martin-Diana

Brunton

. Application of principal component and hierarchical cluster analysis to classify different spices based on in vitro antioxidant activity and individual polyphenolic antioxidant compounds. Journal of Functional Foods. 2011; 3: 179-189. doi: 10.1016/j.jff.2011.03.010.

32.

Rocculi

Romani

Rosa

. Evaluation of physico-chemical parameters of minimally processed apples packed in non-conventional modified atmosphere. Food Research International. 2004; 37: 329-335. doi: 10.1016/j.foodres.2004.01.006.

33.

Kurtanjek

Horvat

Magdic

Kuha

. Factor analysis and modeling for rapid quality assessment of Croatian wheat cultivars with different gluten characteristics. Food Technology and Biotechnology. 2008; 46(3): 270-277.

Design of a high-tech system to evaluate fresh strawberry quality-based on the principal component analysis

Abstract

Keywords

1. Introduction

2. Materials and methods

2.1 Strawberry samples

2.2 Sensory analysis

2.4 Data analysis

2.5 Data standardization

(1) Initialization of quality evaluation indices

(2) Positizing and standardization

3.1 Physical and chemical characteristics of 12 strawberry varieties

Table 1 Descriptive statistics of strawberry physicochemical characteristics

Table 3 Results from the principal component analysis of the first three principal components

Table 5 Result of data standardization of strawberry

Table 7 Composite score and sensory score of twelve strawberry varieties

Footnotes

Acknowledgments

References

Table 1
Descriptive statistics of strawberry physicochemical characteristics

Table 3
Results from the principal component analysis of the first three principal components

Table 5
Result of data standardization of strawberry

Table 7
Composite score and sensory score of twelve strawberry varieties